Customer Value (RFM – Recency, Frequency, Monetary)

There is only one boss. The customer. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else.

Determining the value of a customer is a holy grail in CRM Anaytics. Organizations adopt different methods to rank a customer in terms of value they bring to the organization. Marketing efforts are designed an spent on customers based on value rank.

Two popular methods of determining customer value are

RFM (Recency, Frequency, Monetary)

This is a simple and easy to implement solution

Performs ranking only and parameters can be dynamically adjusted

It requires basic data about a customer’s history of transactions with the organization

CLTV (Customer Life Time Value)

Complex approach and implementing takes more time

Requires more data for analysis to run

Mathematical model does not provide room for changes

Tries to predict future prospects of a customer

Organizations adopt RFM based analysis for customer ranking first and then graduate to CLTV based analysis. RFM analysis deals with three parameters and all three parameters are independent of each other, but are tied together by time context. A common time context should be used or else the final rank will be skewed.

Recency refers to the latest time frame of a customer order. (e.g. For the year 2016, Customer A last order was on 17-Jun-2016 as of 31-Jul-2016)

Frequency refers to the quantum of orders within the referred time frame. (e.g. For the year 2016, Customer A made on average 1.5 orders per month until 31-Jul-2016)

Monetary refers to the value of all customer orders with the referred time frame. (e.g. For the year 2016, Customer A total order value is $12,500 until 31-Jul-2016)

Firstly, historical customer data is used to determine the different scores for R, F & M. Method for determining scores depends on the business policies, which are adjusted based on different parameters. In the example above, assume that a three value scale is implemented i.e. Low = 1, Medium = 2 and High = 3

Recency: If customer had purchased in last 30 days, then “High” or else if purchases were made in last 90 days then “Medium” or else “Low”. In this case, score for Recency is 2.

Frequency: If the customer had purchased in last 30 days 5 or more times, then “High” or else if purchases were made in last 90 days 3 or more times then “Medium” or else “Low”. In this case, score for Frequency is 2.

Monetary: If the customer had purchased in last 30 days value of $5000 or more, then “High” or else if purchases were made in last 90 days value of $10000 or more times then “Medium” or else “Low”. In this case, score for Monetary is 2.

The above example illustrates a simple scoring model for RFM analysis. Based on the scores arrived, a simple rank or weighted rank could be obtained as the product of three parameters as follows.

Simple Rank = 2 * 2 * 2 = 6 out of 9

Weighted Rank = [(50% * 2) + (30% * 2) + (20% * 2)]/100 = 66%

Rank thus determined is used for further analysis and the process is tweaked over period of time whenever there are changes to internal and external factors. In real-time scenarios the scoring models are complex and when used in conjunction with predictive analysis can really give deep insight into customer behavior.